↑ Sample Size ⟶ ↑ Power for GWAS
But this strategy is generally not successful for interaction GWAS
FEV1/FEV1-FVC on 50,008 individuals from UK Biobank (Wain et al. 2015) reveals 0 SNP-smoking interactions (p < 5e-08)One of the alternative strategies is to aggregate genetic variants:
(Renier et al. 2017)
(Aschard et al. 2015)
FEV1/FEV1pp, FVC/FVCpp, FEV1_FVC, pctEmph_Slicer, TLCpp, finalGoldSmokCigNow, CigPerDayNow > 15, CompletedSchool > 237K long (>10kb) local ancestry segments (Parker et al. 2014)
The COPDGene dataset is appropriate, as the proportion of African ancestry is associated with the risk of COPD (Kumar et al. 2010)
Confounding factors other than global ancestry \(a_g\):
FEV1 ~ Age + Age^2 + Gender + Height + PackYears + SmokCigNowpctEmph_Slicer (\(\approx\) 18% of variance)This (marginal) model was used in (Parker et al. 2014).
Is it OK for interaction?
| trait | h2_acs |
|---|---|
| FEV1_FVC_utah | 0.0057 |
| FEV1pp_utah | 0.0105 |
| FEV1_utah | 0.0104 |
| FVCpp_utah | 0.0078 |
| FVC_utah | 0.0079 |
| log_pctEmph_Slicer | 0.0076 |
| TLCpp_race_adjusted | 0.0131 |
\(h^2_{acs} = 2 F_{STC} \theta (1 - \theta) h^2\) (Zaitlen et al. 2014), where
Example: on simulated data on Figure 1 (Zaitlen et al. 2014) \(h^2_{acs}\) 0.032 (s.e. 0.007) corresponds to \(h^2\) 0.83 (s.e. 0.18) (simulated parameters \(F_{STC} = 0.08\) and \(\theta = 0.5\))
Linear mixed-effects model (LMM): \(y \sim X \beta + g + g_{int} + e\) (Sul et al. 2016)
Fixed effects: \(X = [\dots; a_g; a_{l_i}; x_e; a_g * x_e; a_{l_i} * x_e]\)
Random effects:
Unbalanced groups by SmokCigNow / CigPerDaySmokNow:
CigPerDaySmokNow = 0 |
1-14: | >=15 |
|---|---|---|
| 657 (20%) | 1,261 (38%) | 1382 (42%) |
The group CigPerDaySmokNow = 0 has large variance, as all are former smokers
SmokCigNow + ATS_PackYears → Duration_Smoking + log_CigPerDaySmokAvg + SmokCigNow + SmokCigNow0_15 + SmokCigarNowMore details in our previous talk COPDGene African-Americans & QQ plots
SmokCigNow (7 traits)\(z = [z_1; z_2; \dots]^T \sim N(0, \Sigma)\)
under the null hypothesis
2.5| Test | Stat | Law |
|---|---|---|
| Omnibus | \(z^T \Sigma^{-1} z\) | \(\chi^2(p)\) |
| sumZ | \((1^T z)^2 / 1^T \Sigma^{-1} 1\) | \(\chi^2(1)\) |
(Aschard et al. In prep.)
(Province et al, 2013)
Bonferroni 0.05 / 37K = 1.4e-06
| Trait | Exposure | z-score | p-value |
|---|---|---|---|
| FEV1pp | SmokCigNow | 4.5 | 7.3e-06 |
| Omnibus | SmokCigNow | – | 5.7e-05 |
| FEV1_FVC | SmokCigNow | 3.9 | 1.1e-04 |
| FEV1 | SmokCigNow | 3.8 | 1.4e-04 |
Ancestry segment: 2:238,819,792 - 238,904,351
Genes within \(\pm\) 100kb: TWIST2, HDAC4, MIR4440, MIR4441
| Trait | Exposure | z-score | p-value |
|---|---|---|---|
| FEV1 | SmokCigNow0_15 | 4.2 | 2.6e-05 |
| FEV1pp | SmokCigNow0_15 | 4.1 | 4.9e-05 |
| FVC | SmokCigNow0_15 | 4.0 | 6.9e-05 |
| Omnibus | SmokCigNow_15 | – | 1.5e-04 |
| FVCpp | SmokCigNow0_15 | 3.7 | 2.5e-04 |
Predictors are correlated
However, the threshold depends on the genetic architecture, e.g. heritability (Joo etl al. 2016)
| Data | Min size | Mean size | Genome coverage |
|---|---|---|---|
| Local ancestry | 10kb | 13kp | 74% |
| ENCODE annotation | – | 0.150kb | 10% |
| Intersection | 10kb | 11kb | 70% |
Hypothesis on the mechanism of interaction:
Smoking → Up/Down Methylation → COPD-related phenotype
Wan et al., Smoking-associated site-specific differential methylation in buccal mucosa in the COPDGene study (2015)
Genome-wide interaction scan on local ancestry has potential to discover new gene-environment interactions
(the number of tests is decreased dramatically)
In the future, we plan to gain insights on the interaction mechanism (collaboration work)
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———. 2017. “Evidence for large-scale gene-by-smoking interaction effects on pulmonary function.” International Journal of Epidemiology 46 (3): 894–904. doi:10.1093/ije/dyw318.
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